检索结果-内蒙古大学图书馆

2024 IEEE Region 10 Conference, TENCON 2024

作者： Soumya, A. Krishna Mohan, C. Cenkeramaddi, Linga Reddy Babu, Sobhan Indian Institute of Technology Department of Computer Science and Engineering Hyderabad India University of Agder Department of Information and Communication Technology Grimstad Norway

ISBN: (纸本)9798350350821

Autonomous vehicles increasingly rely on accurate three-dimensional (3D) object detection for safe navigation. While two-dimensional (2D) methods offer computational efficiency, the shift to 3D detection enhances precision in understanding environments. Point-based and voxel-based approaches are accurate but computationally intensive for onboard deployment. Pillar-based methods like PointPillars provide efficiency but may lack detection accuracy compared to voxel-based approaches. In this paper, we focus on enhancing the performance of PointPillars, a popular pillar-based detector, using LiDAR data. The proposed approach (PointBi-FPN) employs a bi-directional feature pyramid network (Bi-FPN) as a backbone, that aggregates multiscale features in the input data, providing a holistic view of the environment, which is crucial for detecting objects of varying sizes and distances accurately. Bi-FPN improves the model's understanding of complex scenes, making it more robust to occlusions. Through extensive experimentation on the KITTI dataset, the PointBi-FPN approach demonstrates better performance across all three detection benchmarks: BEV (Bird's Eye View), 3D (Three-Dimensional), and AOS (Average Orientation Similarity). Notably, the proposed approach exhibits significant improvements, particularly in accurately detecting objects labeled as 'hard' difficulty. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Reconstruct Dense Live-Cell Microscopy Images via Learning Continuous Fluorescence Field 22

Reconstruct Dense Live-Cell Microscopy Images via Learning C...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Cao, Jianfeng Duan, Bin Yan, Hong School of Biomedical Engineering Harbin Institute of Technology Shenzhen China Illinois Institute of Technology Department of Computer Science United States City University of HongKong Department of Electrical Engineering Hong Kong

ISBN: (纸本)9798331520526

Live-cell imaging plays an increasingly important role in developmental biology and drug discovery. The phototoxity of laser light, however, inevitably induces a tradeoff between image quality and cell viability. While learning-based denoising models have been proposed for improving noisy low-quality images, it is practically challenging to obtain high-quality training reference in live-cell imaging. In this work, we present a reference-free framework MicroNeRF for reconstructing dense image stack from sparse slices. By formalizing the fluorescence field as an implicit neural representation, high-quality stack can be posteriorly sampled from continuous manifold. Additionally, we address the interference among fluorescent signals through an adaptive learnable point spread function. Experimental results demonstrate the superiority of MicroNeRF in sparse reconstruction and downstream segmentation task. © 2025 IEEE.

关键词： Optical transfer function

来源：评论

学校读者我要写书评

暂无评论

Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization 3

Utilizing Machine Learning for Sport Data Analytics in Crick...

引用

3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Suguna, R. Praveen Kumar, Y. Suriya Prakash, J. Neethu, P.S. Kiran, S. Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vidhya Jyothi Institute of Technology Department of Computer Science and Engineering Hyderabad India Department of Computer Science and Engineering Bengaluru India School of Engineering and Technology Bengaluru India Nitte Meenakshi Institute of Technology Department of Mathematics Bangalore India

ISBN: (纸本)9798350340358

Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach for Underwater Image Enhancement 8

A Hybrid Approach for Underwater Image Enhancement

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Abin, Deepa Surdas, Spandan Suryawanshi, Atharva Thakare, Khushi Saravane, Yash Vishwakarma Institute of Technology Department of Ai & Ds Pune India Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350376425

Underwater imaging presents significant challenges primarily due to adverse environmental conditions, including insufficient light, hazy visibility, and light scattering, which result in uneven colors. Research in this field focuses on enhancing specific attributes of images, such as blurriness reduction, color restoration, and contrast enhancement. However, the combination of these algorithms does not always yield visually pleasing images or the desired outcomes. This paper presents experiments on various combinations of algorithms to determine the fusion method that produces consistent and intended results. Techniques explored include dehazing with boundary constraint and contextual regularization, integrated color models, gray world adjustment, and histogram equalization, along with their combinations, to identify the fusion method that demonstrates the greatest improvement. The results indicate that, despite variations in the quality of input images, the proposed fusion procedure consistently delivers superior outcomes. © 2024 IEEE.

关键词： Underwater imaging

来源：评论

学校读者我要写书评

暂无评论

An Innovative Method to Detect Cyberbullying on Social Media 3

An Innovative Method to Detect Cyberbullying on Social Media

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Muthumanickam, K. Geetha, M. Mahesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking"perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Dual-Model Approach for Lung Disease Classification using Convolutional Neural Networks and Support Vector Machines 2

Dual-Model Approach for Lung Disease Classification using Co...

引用

2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shaik, Mohammed Ali Fatima, Afshan Parveen, Masrath Rani, A. Soumya Mohammad, Akheel Rahim, Abdul School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India Department of Information Technology Vidya Jyothi Institute of Technology Hyderabad India Hyderabad India Department of Computer Science & Engineering Dr. Vrk women's College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Secunderabad India

ISBN: (纸本)9798331504960

This paper proposes a solution of integrating the use of Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) for categorizing lung conditions of people with and without lung disease. The proposed model has achieved a high F1-Score of 97.4%, a high Precision of 93.17% and high Recall of 93. 63 for the Healthy class. The F1 Scores of this study for the Infected Lung class were as follows: the accuracy of F1 Score was 97.4%;Precision was 94.12%;and Recall was 92.06%. The combining of SVM and CNN model was quite accurate with the accuracy of 97% and the model was accurate in multiple domains of assessment. These results suggest that the application of CNNs and SVMs to the task of medical image analysis constitutes a promising and accurate approach for diagnosing lung diseases. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Efficiency of Dual-Zone Registration System with Timer Mechanism in Wireless Networks 3

Evaluating the Efficiency of Dual-Zone Registration System w...

引用

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Raman, Ramakrishnan Vinoth, T. Barve, Amit Tiwari, Vinita Pune India Chennai Institute of Technology Department of Mechatronics Engineering Kundrathur Chennai India Paruluniversity Faculty of Engineering and Technology Department of Computer Science and Engineering Vadodara India Pune India

ISBN: (纸本)9798331508456

The increasing demand for efficient communication in wireless networks necessitates optimized registration systems that ensure smooth connectivity and scalability. This paper introduces a novel Dual-Zone Registration System with a Dynamic Timer Mechanism designed to enhance registration efficiency in wireless networks. The system classifies devices into two zones: a primary zone for immediate registration and a secondary zone for devices requiring delayed registration. Leveraging a dynamic timer mechanism, the system regulates registration attempts to reduce congestion and optimize resource allocation. Simulations reveal that the proposed system significantly improves key performance metrics, including registration success rate, average registration time, packet loss, and network load. The dual-zone system achieved a 95% registration success rate, reducing packet loss to 3% compared to 12% in centralized systems and 8% in simple timer-based systems. Average registration time decreased to 6.3 seconds, outperforming centralized (12.5 seconds) and simple timer-based systems (9.8 seconds). Additionally, the system maintained a lower network load of 45 requests per second, showcasing its scalability and efficiency. By dynamically adjusting registration delays based on real-time network conditions, the proposed system ensures efficient resource utilization and balanced load distribution, making it a promising solution for large-scale wireless networks. These findings validate the effectiveness of the dual-zone approach in improving the scalability, reliability, and overall performance of modern wireless networks. © 2025 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

FACE UNLOCK SYSTEM FOR VEHICLES 2

FACE UNLOCK SYSTEM FOR VEHICLES

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Navya, Soleti Suresh, Juttu Sangeetha, A. Praveen, G. Nabi, Shaik Abdul Kumar, Dilip Mlr Institute of Technology Department of Computer Science and Information Technology India Malla Reddy Engineering College and Management Sciences Department of Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Cse Dept Avniet Jntuh India Mlr Institute of Technology Dept. of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350370188

Currently, all vehicles are key operated. Our intention is therefore to develop an intelligent unlocking system based on facial recognition technology. In Today's era of automation and smart devices, privacy and security are important issues in information technology so to information system, so it is urgent to change system security measures. In previous systems, many vehicle doors had mechanical locks with a limited number of keys. To enhance security, we propose an intelligent vehicle door unlocking system based on facial recognition the system uses camera sensor to recognize faces and image matching algorithms to recognize authenticated faces. Only people with matching faces can open the door this system not only provides additional security but also useful for systems without physical keys. © 2024 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Statistical inference on multicomponent stress–strength reliability with non-identical component strengths using progressively censored data from Kumaraswamy distribution

引用

Soft Computing 2024年第17-18期28卷 9317-9339页

作者： Saini, Shubham Patel, Jyoti Garg, Renu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India Machine Intelligence Unit Indian Statistical Institute Kolkata700108 India Department of Statistics Ramanujan College Delhi110019 India

In this article, we draw inferences on stress–strength reliability in a multicomponent system with non-identical strength components based on the progressively censored data from the Kumaraswamy distribution (KuD). When one shape parameter of KuD is known, the uniformly minimum variance unbiased estimator is produced. To evaluate the reliability of such systems when all the parameters are unknown, the maximum likelihood and Bayes estimators are developed. Along with coverage probabilities, the asymptotic confidence and highest posterior credible (HPD) intervals are also obtained. Tierney–Kadane’s approximation and Markov chain Monte Carlo methods are used for Bayesian computations. To compare the performance of estimators, a Monte Carlo simulation study is performed. Also, one real-life example is analyzed for illustrative purposes. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Compromising the Intelligence of Modern DNNs: On the Effectiveness of Targeted RowPress

Compromising the Intelligence of Modern DNNs: On the Effecti...

引用

2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Zhou, Ranyang Liu, Jacqueline T. Ahmed, Sabbir Angizi, Shaahin Rakin, Adnan Siraj New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ United States State University of New York Department of Computer Science BinghamtonNY United States

ISBN: (纸本)9783982674100

Recent advancements in side-channel attacks have revealed the vulnerability of modern Deep Neural Networks (DNNs) to malicious adversarial weight attacks. The well-studied RowHammer attack has effectively compromised DNN performance by inducing precise and deterministic bit-flips in the main memory (e.g., DRAM). Similarly, RowPress has emerged as another effective strategy for flipping targeted bits in DRAM. However, the impact of RowPress on deep learning applications has yet to be explored in the existing literature, leaving a fundamental research question unanswered: How does RowPress compare to RowHammer in leveraging bit-flip attacks to compromise DNN performance? This paper is the first to address this question and evaluate the impact of RowPress on DNN applications. We conduct a comparative analysis utilizing a novel DRAM-profile-aware attack designed to capture the distinct bit-flip patterns caused by RowHammer and RowPress. Eleven widely-used DNN architectures trained on different benchmark datasets deployed on a Samsung DRAM chip conclusively demonstrate that they suffer from a drastically more rapid performance degradation under the RowPress attack compared to RowHammer. The difference in the underlying attack mechanism of RowHammer and RowPress also renders existing RowHammer mitigation mechanisms ineffective under RowPress. As a result, RowPress introduces a new vulnerability paradigm for DNN compute platforms and unveils the urgent need for corresponding protective measures. © 2025 EDAA.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：